Pragmatic Trial of Remote tDCS and Somatosensory Training for Phantom Limb Pain with Machine Learning to Predict Treatment Response

利用机器学习预测治疗反应的远程 tDCS 和体感训练治疗幻肢痛的实用试验

• 批准号: 10434306
• 负责人: Felipe Fregni
• 金额: $ 61.21万
• 依托单位: SPAULDING REHABILITATION HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2027-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10434306
• 关键词: Address; Affect; Anodes; Anterior; Area; Behavior Therapy; Behavioral; Biological Markers; Brain; Characteristics; Chronic; Clinical; Combined Modality Therapy; Conduct Clinical Trials; Control Groups; Data; Devices; Disease; Effectiveness; Esthesia; Force of Gravity; Goals; Hand; Health Professional; Home; Individual; Intervention; Investigation; Knowledge; Life; Machine Learning; Meta-Analysis; Modernization; Motor; Motor Cortex; Movement; Occupational Therapy; Operative Surgical Procedures; Pain; Pain management; Paper; Patients; Phantom Limb; Phantom Limb Pain; Pharmacological Treatment; Pharmacology; Phenotype; Physical therapy; Population Decreases; Prediction of Response to Therapy; Protocols documentation; Publishing; Randomized; Rehabilitation therapy; Research; Resistance; Surrogate Markers; Syndrome; Techniques; Testing; Therapeutic; Training; Underrepresented Populations; Validation; Visit; base; chronic neuropathic pain; chronic pain; clinical predictors; cognitive training; cost; design; effectiveness evaluation; effectiveness testing; efficacy testing; evidence base; experience; functional restoration; gray matter; heart rate variability; improved; indexing; innovation; machine learning algorithm; machine learning method; neural circuit; neuroimaging; neurological rehabilitation; neuronal circuitry; neurophysiology; neuroregulation; noninvasive brain stimulation; novel; pain patient; pain reduction; pain relief; painful neuropathy; portability; pragmatic trial; predicting response; predictive test; relating to nervous system; remote assessment; remote therapy; research study; residual limb; response; response biomarker; somatosensory; spinal cord injury pain; statistical and machine learning; treatment as usual; treatment response; treatment strategy; trial design

PROJECT SUMMARY/ABSTRACT: Phantom limb pain (PLP) is considered an extremely hard-to-treat disorder, given that traditional treatments are not effective in targeting the maladaptive neuronal circuits associated with chronic pain. Transcranial direct current stimulation (tDCS) is a non-invasive, safe brain stimulation technique that has been shown to revert maladaptive plasticity as well as reduce pain in neuropathic pain and other pain syndromes. Our previous R01 on this topic has demonstrated the efficacy of tDCS combined with somatosensory training in a controlled setting to improve pain and that this intervention changes PLP associated cortical plasticity. Our previous R01 also shown pain phenotypes based on PLP characteristics that are more responsive to this treatment. The objective of this renewal is to provide novel data to address critical knowledge gaps such as (i) testing a portable device that would reach underrepresented populations; (ii) validation of this therapy in a more pragmatic setting; (iii) confirmation and testing of predictors of response with statistical and machine learning techniques; and (iv) testing the parasympathetic tone changes (with the remote assessment) as a biomarker of neuropathic pain relief. The central hypothesis is that a combination of home-based tDCS and somatosensory therapy will reduce pain in PLP patients. Our long-term goal is to develop a cheap, efficacious, safe, and practical treatment for PLP. Our rationale is that understanding the effects of tDCS in a real-life setting will validate this treatment for PLP and identify predictors of response to this treatment will help health professionals better target and more precisely treat individuals with this condition. Our specific aims will test the following hypotheses: (Aim 1) tDCS combined with somatosensory therapy will be associated with a significantly larger effect size (of at least 1) compared to the control condition in pain reduction; (Aim 2) identifying predictors of response of this combined treatment using machine learning algorithms will help identify different pain phenotypes in patients with PLP and improve their target treatment; (Aim 3) combined treatment will bolster the parasympathetic tone (as indexed by higher heart rate variability) and reduce sympathetic activation, changes which will be correlated with PLP decreases. This contribution is significant because, although several studies have tested the efficacy of tDCS for chronic pain, there is a need to evaluate its effectiveness in a real-world setting, and this proposal provides critical data to develop a safe and unique intervention to be applied at home, which can therefore increase its access to underrepresented populations and decrease therapeutic costs. This investigation will also provide mechanistic data on predictors of response and changes in parasympathetic tone associated with this intervention. The proposed research is innovative because it offers a pragmatic trial design for PLP treatment and aims to validate a home-based tDCS device that is feasible and able to provide longer treatments remotely. This proposal is also investigating HRV as a surrogate marker for reducing PLP and assessing its feasibility in a real-life setting. Finally, this proposal validates predictors of response to PLP treatment using machine learning algorithms.

项目摘要/摘要： 幻影肢体疼痛（PLP）被认为是一种极难治疗的疾病，鉴于传统治疗是 无效靶向与慢性疼痛有关的不良适应性神经元回路。经颅直接 电流刺激（TDCS）是一种无创，安全的脑刺激技术，已证明可以恢复 适应不良的可塑性以及减轻神经性疼痛和其他疼痛综合征的疼痛。我们以前的R01 关于这个主题，在受控的环境中，TDCS的功效与体感培训相结合 为了改善疼痛，这种干预会改变PLP相关的皮质可塑性。我们以前的R01也 显示了基于PLP特征的疼痛表型，这些特征对此治疗更敏感。目标 此续约是提供新的数据来解决关键知识差距，例如（i）测试便携式设备 那将达到代表性不足的人群； （ii）在更务实的环境中验证这种疗法； （iii） 用统计和机器学习技术确认和测试响应预测因素； （iv） 测试副交感神经的变化（远程评估）作为神经性疼痛的生物标志物 宽慰。中心假设是，基于家庭的TDC和体感疗法的组合将减少 PLP患者的疼痛。我们的长期目标是为PLP开发廉价，有效，安全和实用的治疗方法。 我们的理由是，了解TDC在现实生活环境中的影响将验证PLP的这种治疗方法 并确定对此治疗的反应预测指标将有助于卫生专业人员更好地目标，更精确 治疗这种情况。我们的具体目的将检验以下假设：（ AIM 1）TDC合并 与体感疗法相比 减轻疼痛的控制条件； （AIM 2）确定这种联合治疗的反应预测指标 使用机器学习算法将有助于鉴定PLP患者的不同疼痛表型并改善 他们的目标治疗； （AIM 3）联合治疗将增强副交感神经（如较高的索引） 心率变异性）并减少交感神经激活，将与PLP相关的变化减少。 这种贡献很重要，因为尽管有几项研究已经测试了TDC对慢性的功效 痛苦，需要评估其在现实环境中的有效性，该提案提供了关键数据 为了制定安全和独特的干预措施以在家中应用，因此可以增加其访问权限 代表性不足的人群和降低治疗成本。这项调查还将提供机械 有关响应的预测因素和与此干预相关的副交感神经的变化的数据。这 拟议的研究具有创新性，因为它为PLP治疗提供了务实的试验设计，旨在验证 可行的基于家庭的TDCS设备，能够远程提供更长的处理。该提议也是 调查HRV作为降低PLP并评估现实生活中的可行性的替代标记。 最后，该提案使用机器学习算法验证了对PLP治疗的反应预测指标。